Acquiring sharply-focused images of moving people or objects is a fundamental and challenging problem in several surveillance applications, particularly iris-based biometrics and face recognition. Biometric identification or verification of individuals can be performed on a number of input modalities. Several modalities such as iris and face recognition depend on image capture that, in different environments, may have degraded image quality. Image blur, due to subject motion or optical defocus, is an important type of degradation that is commonly encountered in biometric systems. When blur cannot be mitigated by changing the image capture technique, it becomes necessary to process blurred images in order to produce sharply-focused images that can be used for biometric identification.
Some deblurring approaches have been implemented. For example, a non-limiting example of a deblurring technology is disclosed in U.S. Patent Application Publication Serial No. US2007/0258707A1, entitled “Method and Apparatus for Deblurring Images,” which published to Ramesh Raskar on Nov. 8, 2007, and is incorporated herein by reference. Another deblurring approach is disclosed in U.S. Patent Application Publication Serial No. US2007/0258706A1, entitled “Method for Deblurring Images Using Optimized Temporal Coding Patterns,” which published to Ramesh Raskar, et al on Nov. 8, 2007, and is incorporated herein by reference.
While there are many techniques for performing deblurring, all such approaches require an estimate of the blur magnitude. Methods that remove motion blur additionally require estimates of the motion direction/path and the shape of the blur, which is related to the camera's shutter pattern and illumination characteristics. Methods that remove optical blur require estimates of both the magnitude and the shape of the blur, the shape being related to optical characteristics. In its full generality, the blur estimation problem is known to be ill-posed on a single image.